Field of the Subject Disclosure
The present subject disclosure relates to imaging for medical diagnosis. More particularly, the present subject disclosure relates to automatic field of view (FOV) selection on a whole slide image.
Background of the Subject Disclosure
In the analysis of biological specimens such as tissue sections, blood, cell cultures and the like, biological specimens are stained with one or more combinations of stains to identify, for example, biomarkers, cells or cellular structures, and the resulting assay is viewed or imaged for further analysis. Observing the assay enables a variety of processes, including diagnosis of disease, assessment of response to treatment, and development of new drugs to fight disease. An assay includes one or more stains conjugated to an antibody that binds to protein, protein fragments, or other objects of interest in the specimen, hereinafter referred to as targets or target objects. Some biomarkers, for example, have a fixed relationship to a stain (e.g., the often used counterstain hematoxylin), whereas for other biomarkers, a stain may developed or anew assay may be created. Subsequent to staining, the assay may be imaged for further analysis of the contents of the tissue specimen. An image of an entire slide is typically referred to as a whole-slide image, or simply whole-slide.
Typically, in immunoscore computations, a scientist uses a multiplex assay that involves staining one piece of tissue or a simplex assay that involves staining adjacent serial tissue sections to detect or quantify, for example, multiple proteins or nucleic acids etc. in the same tissue block. With the stained slides available, the immunological data, for instance, the type, density and location of the immune cells, can be estimated from the tumor tissue samples. It has been reported that this data can be used to predict the patient survival of colorectal cancer and demonstrates important prognostic role.
In the traditional workflow for immunoscore computation, the expert reader such as a pathologist or biologist selects the representative fields of view (FOVs) or regions of interest (ROIs) manually, as the initial step, by reviewing the slide under a microscope or reading an image of a slide, which has been scanned/digitized, on a display. When the tissue slide is scanned, the scanned image is viewed by independent readers and the FOVs are manually marked based on the readers' personal preferences. After selecting the FOVs, a pathologist/reader manually counts the immune cells within the selected FOVs. Manual selection of the FOVs and counting is highly subjective and biased to the readers, as different readers may select different FOVs to count. Hence, an immunoscore study is no longer reproducible.
EP2546802 features an artificial hyper-spectral image generated from co-registered tissue slides that enables the sophisticated co-analysis of image stacks. Co-registration is performed on tiles of high-resolution images of tissue slices, and image-object statistics are used to generate pixels of a down-scaled hyper-spectral image. The method of analyzing digital images to generate hyperspectral images combines two hyperspectral images to generate a third hyperspectral image.
As another example, U.S. Publication No. 2014/0180977 features classifying histological tissues or specimens with two phases. In a first phase, the method includes providing off-line training using a processor during which one or more classifiers are trained based on examples. In a second phase, the method includes applying the classifiers to an unknown tissue sample with extracting the first set of features for all tissue units; deciding for which tissue unit to extract the next set of features by finding the tissue unit for which a score is maximized; iterating until a stopping criterion is met or no more feature can be computed; and issuing a tissue-level decision based on a current state.
In some examples, an automated workflow was performed where quantitative image analysis results of consecutive differently stained tissue sections are locally fused by co-registration. The results are spatially resolved feature vectors containing features which are hyperspectral. Heat maps with many layers (hyperspectral) are generated from this data, revealing relationships between different stains that would not be evident from single stains alone.
In other examples, digital image analysis was performed to evaluate inter- and intratumor heterogeneity, and correlate protein expression with histologic features, including a histopathologic assessment of tumor activity, defined by nuclear chromatin density ratio (CDR). Pathologic assessment of tumor activity and digital assessment of average nuclear size and CDR were all significantly correlated.